论文标题
基于支持向量机的二进制响应回归模型
A binary-response regression model based on support vector machines
论文作者
论文摘要
软砂支持向量机（SVM）是用于预测二进制响应数据的普遍工具。但是，SVM完全通过数值优化问题而不是概率模型来表征，因此不会直接生成概率推论语句作为输出。我们考虑了基于表征SVM的优化问题的二进制响应数据的概率回归模型。在弱规则性假设下，我们证明我们模型的最大似然估计（MLE）存在，并且它是一致且渐近正常的。我们通过仿真研究进一步评估了模型的性能，并证明了它在有关垃圾邮件检测和井水获取的真实数据应用中的使用。
The soft-margin support vector machine (SVM) is a ubiquitous tool for prediction of binary-response data. However, the SVM is characterized entirely via a numerical optimization problem, rather than a probability model, and thus does not directly generate probabilistic inferential statements as outputs. We consider a probabilistic regression model for binary-response data that is based on the optimization problem that characterizes the SVM. Under weak regularity assumptions, we prove that the maximum likelihood estimate (MLE) of our model exists, and that it is consistent and asymptotically normal. We further assess the performance of our model via simulation studies, and demonstrate its use in real data applications regarding spam detection and well water access.
